Privacy

Two recent papers, one from West Point
Academy,
and one by a collection of authors at Tel Aviv University and Cornell
Tech, lay out methods for
identifying videos by performing straightforward traffic analysis on
encrypted data streams. One approach opens the door for snooping by any
party that has direct access to the network on which a user is watching
videos, such as an ISP or a VPN provider. The other could be used by any
attacker who is able to deliver malicious Javascript code to the user’s
browser. But both inspect the size of data bursts being transferred across
the user’s network in order to fingerprint individual videos and compare
them to a database of known, previously characterized content.

School children are being spied on by tech companies through devices and
software used in classrooms that often collect and store kids’ names, birth
dates, browsing histories, location data, and much more—often without
adequate privacy protections or the awareness and consent of parents [...]

Tech

Already, mathematical models are being used to help determine who makes
parole, who's approved for a loan, and who gets hired for a job. If you
could get access to these mathematical models, it would be possible to
understand their reasoning. But banks, the military, employers, and others
are now turning their attention to more complex machine-learning
approaches that could make automated decision-making altogether
inscrutable. Deep learning, the most common of these approaches,
represents a fundamentally different way to program computers. "It is a
problem that is already relevant, and it’s going to be much more relevant
in the future," says Tommi Jaakkola, a professor at MIT who works on
applications of machine learning. "Whether it's an investment decision, a
medical decision, or maybe a military decision, you don't want to just
rely on a 'black box' method."